Fingerprint-Based Localization Using Commercial LTE Signals: A Field-Trial Study

Abstract

Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively.

Is Centimeter Accuracy Achievable for LTE-CSI Fingerprint-Based Indoor Positioning?

“…Except for channel characteristics, the design of CSI fingerprinting system has a major impact on the positioning accuracy. In this section, we examine the design aspects that affect the positioning accuracy by comparing the CSI fingerprinting system designed for Wi-Fi systems with centimeter-level accuracy [4] and the CSI fingerprinting systems designed for LTE systems with meter-level accuracy [6], [7], respectively. The aim is to guide us to build a high-accuracy LTE-CSI fingerprinting system.…”

“…There have been numerous studies with respect to Wi-Fi systems in the literature, where various CSI fingerprinting methods have been proposed [1] and the centimeter-level positioning accuracy has been reported [4]. For LTE systems, there are only few works on fingerprint based indoor positioning, e.g., [5]- [7]. In [5] the fingerprints are generated based on reference signal received power (RSRP) and reference signal received quality (RSRQ) rather than CSI, and thus far [6] and…”

“…The associate editor coordinating the review of this manuscript and approving it for publication was Amjad Mehmood . [7] are the only two works studying the LTE-CSI fingerprinting. We conjecture that the very little attention paid to the LTE-CSI fingerprinting in the literature is mainly caused by the difficulty of acquiring the LTE CSI.…”

“…For LTE systems, although the RSRP and RSRQ can be readily obtained, e.g., via the application program interface (API) of some mobile phones, off-theshelf equipment for CSI acquisition is still unavailable. For example, the SDR platform is employed in [6] and [7] while we employ the ZedBoard, both of which require the implementation of LTE baseband processing via either software or hardware.…”

“…The first results of the LTE-CSI fingerprinting are presented in [6], where the positioning accuracy is on the meter level, comparable to the RSRP fingerprinting. The latest LTE-CSI fingerprinting results are provided in [7], where the mean distance errors of 0.47 and 19.9 meters are achieved for indoor and outdoor scenarios, respectively. The performance is much worse than the centimeter-level accuracy achieved by the Wi-Fi CSI fingerprinting as reported in [4].…”